MovieReview: 
Table REF  gives results on the movie review task. Since the human rationales are multiple phrase pieces, we make both Rnp and A2r perform token-level selections to better fits to this task. We follow the standard setting [1]}, [2]} to use the sparsity and continuity constraints to regularize the selected rationales for all methods. For fair comparisons, we use a strong constraint weight of 1.0 to punish all algorithms that highlight more than 20% of the inputs, or have more than 10 isolated spans. These numbers are selected according to the statistics of the rationale annotations.
<TABLE>